> For the complete documentation index, see [llms.txt](https://docs.consentiumiot.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.consentiumiot.com/readme/edgemodelkit-sensor-data-acquisition-and-logging-library.md).

# EdgeModelKit: Sensor Data Acquisition and Logging Library

EdgeModelKit is a Python library developed by **EdgeNeuron**, designed to simplify sensor data acquisition, logging, and real-time processing for IoT devices. It works seamlessly with the **DataLogger script** from the [EdgeNeuron Arduino library](https://github.com/ConsentiumIoT/EdgeNeuron), and now supports **HTTP-based acquisition** for devices that expose REST APIs.

***

## **Features**

* **Serial Communication**: Supports data acquisition over serial ports with robust error handling.
* **Flexible Data Fetching**: Retrieve sensor data as Python lists or NumPy arrays.
* **Customizable Logging**: Log sensor data into CSV files with optional timestamps and counters.
* **Class-Based Organization**: Log data with class labels to prepare datasets for machine learning tasks.
* **Error Handling**: Gracefully handles data decoding errors and missing keys in sensor data packets.

***

## **Usage Prerequisites**

This library is designed to work with the **DataLogger script** available in the [EdgeNeuron Arduino library](https://github.com/ConsentiumIoT/EdgeNeuron). The DataLogger script configures your Arduino-based IoT device to send structured JSON sensor data over a serial connection.

Before using EdgeModelKit, ensure:

1. Your Arduino device is programmed with the **DataLogger script** from the [EdgeNeuron Arduino library](https://github.com/ConsentiumIoT/EdgeNeuron).
2. The device is connected to your system via a serial interface.

***

## **Installation**

Install EdgeModelKit using pip:

```bash
pip install edgemodelkit  
```

***

## **Quick Start**

### **1. Initialize the DataFetcher**

```python
from edgemodelkit import DataFetcher  

# Initialize the DataFetcher with the desired serial port and baud rate  
fetcher = DataFetcher(serial_port="COM3", baud_rate=9600)  
```

### **2. Fetch Sensor Data**

```python
# Fetch data as a Python list  
sensor_data = fetcher.fetch_data(return_as_numpy=False)  
print("Sensor Data:", sensor_data)  

# Fetch data as a NumPy array  
sensor_data_numpy = fetcher.fetch_data(return_as_numpy=True)  
print("Sensor Data (NumPy):", sensor_data_numpy)  
```

### **3. Log Sensor Data**

```python
# Log 10 samples to a CSV file with timestamp and count columns  
fetcher.log_sensor_data(class_label="ClassA", num_samples=10, add_timestamp=True, add_count=True)  
```

***

## **CSV Logging Details**

The CSV file is generated automatically based on the sensor name (e.g., `TemperatureSensor_data_log.csv`) and contains the following:

* **Timestamp**: (Optional) Records the time when the data was logged.
* **Sample Count**: (Optional) A sequential counter for each data sample.
* **Data Columns**: Each element in the sensor data array is stored in separate columns (e.g., `data_value_1`, `data_value_2`, ...).

The data is saved under a folder named `Dataset`, with subfolders organized by `class_label` (if specified).

***

## **Real-Time Data Processing Example**

```python
from edgemodelkit import DataFetcher  

fetcher = DataFetcher(serial_port="COM3", baud_rate=9600)  

try:  
    while True:  
        # Fetch data as NumPy array  
        sensor_data = fetcher.fetch_data(return_as_numpy=True)  
        print("Received Data:", sensor_data)  

        # Perform custom processing (e.g., feed to a TensorFlow model)  
        # prediction = model.predict(sensor_data)  
        # print("Prediction:", prediction)  
finally:  
    fetcher.close_connection()  
```

***

## **Dependencies**

EdgeModelKit requires the following Python packages:

* `numpy`
* `pandas`
* `pyserial`
* `json`

Install dependencies with:

```bash
pip install numpy pandas pyserial json  
```

***

## **Contributing**

We welcome contributions to EdgeModelKit! Feel free to submit bug reports, feature requests, or pull requests on our [GitHub repository](https://github.com/ConsentiumIoT/edgemodelkit).

***

## **License**

EdgeModelKit is licensed under the MIT License. See the LICENSE file for details.

***

## **Support**

For support and inquiries, contact us at **<support@edgeneuronai.com>** or visit our [GitHub repository](https://github.com/ConsentiumIoT/edgemodelkit).

***

## **About EdgeNeuron**

EdgeNeuron is a pioneer in edge computing solutions, enabling developers to build intelligent IoT applications with state-of-the-art tools and libraries. Learn more at [edgeneuronai.com](https://edgeneuronai.com).

***